One of the most frustrating problems facing grocery retailers is the lack of an automatic, accurate and efficient method to identify fruits and vegetables at the checkout lane. Many manual methods exist, but all are slow and inaccurate. The most common method involves consulting a printed list of all fruits and vegetables sold in a given store, to find their corresponding price codes.
Since the process is so inefficient, many cashiers simply guess at the price codes or simply memorize and use a small subset of common price codes—especially if there are long lines at the register. This means the retailer loses revenue if the item actually sold is a more expensive item. For this reason, speed and accuracy are both important. In a self-service checkout transaction, customers often guess or sometimes deliberately defraud the system.
A successful automatic produce recognition system needs to solve two challenges: (1) select appropriate and discriminative features (for example shape, size, color, aroma, and the like) for produce modeling; and (2) build an efficient and robust classifier.
There have been attempts at solving this problem, including analysis of spectral response of reflected light, analysis of camera images, and analysis of produce aromas but these attempts have proven unreliable at consistently identifying some items.
Since it is unlikely that any one approach will be sufficient to guarantee accurate recognition of all items, recognition methodologies have been combined. However, increasing the number of different kinds of recognition methodologies complicates classifier design.
For example, a spectral-based methodology may use a distance-measure-based Gaussian density classifier with checkout frequency. Image-based methodologies may use a nearest neighbor classifier. Both methodologies are simple and easy to update, but they treat all features equally and independently. These simplistic assumptions do not reflect reality, where signals are correlated (not independent), and not all features are equally important. Some features are more important and provide better accuracy for produce recognition than others. Finally, these classifiers have inputs that are specifically configured for the type of feature used, and may not be suitable for use with a feature provided by a future sensor or technology.
It would be desirable to provide a produce recognition method which addresses these concerns.